summary.singletable: Summarize the objects 'singletable'

Description Usage Arguments Value References See Also Examples

View source: R/summary.r

Description

Summary a model of class singletable fitted by singletable.

Usage

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      ## S3 method for class 'singletable'
summary(object,...) 

Arguments

object

an object inheriting from class singletable.

...

additional arguments; currently none is used.

Value

A list with the following components: posterior mean, posterior median, equal tail CI, and HDR CI.

References

Luo, S., Chen, Y., Su, X., Chu, H., (2014). mmeta: An R Package for Multivariate Meta-Analysis. Journal of Statistical Software, 56(11), 1-26.

Chen, Y., Luo, S., (2011a). A Few Remarks on "Statistical Distribution of the Difference of Two Proportions' by Nadarajah and Kotz, Statistics in Medicine 2007; 26(18):3518-3523" . Statistics in Medicine, 30(15), 1913-1915.

Chen, Y., Chu, H., Luo, S., Nie, L., and Chen, S. (2014a). Bayesian analysis on meta-analysis of case-control studies accounting for within-study correlation. Statistical Methods in Medical Research, doi: 10.1177/0962280211430889. In press.

Chen, Y., Luo, S., Chu, H., Su, X., and Nie, L. (2014b). An empirical Bayes method for multivariate meta-analysis with an application in clinical trials. Communication in Statistics: Theory and Methods. In press.

Chen, Y., Luo, S., Chu, H., Wei, P. (2013). Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials. Statistics in Biopharmaceutical Research, 5(2), 142-155.

See Also

multipletables

Examples

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# Inference under Jeffreys prior distribution
#single.OR.Jeffreys <- singletable(a1=0.5, b1=0.5, a2=0.5,
#                                  b2=0.5, y1=40, n1=96, y2=49, n2=109,
#                                  model="Independent",
#                                  measure="OR", method="exact")
#summary(single.OR.Jeffreys)

# Inference under Laplace prior distribution
#single.OR.Laplace <- singletable(a1=1, b1=1, a2=1, b2=1,
#                                 y1=40, n1=96, y2=49, n2=109,
#                                 model="Independent", measure="OR",
#                                 method="exact")
# Inference under Sarmanov prior distribution with positive correlation
#single.OR.Sar1 <- singletable(a1=0.5, b1=0.5, a2=0.5, b2=0.5,
#                              rho=0.5, y1=40, n1=96, y2=49, n2=109,
#                              model="Sarmanov",
#                              measure="OR", method="exact")
# Inference under Sarmanov prior distribution with negative correlation
#single.OR.Sar2 <- singletable(a1=0.5, b1=0.5, a2=0.5, b2=0.5,
#                              rho=-0.5, y1=40, n1=96, y2=49, n2=109,
#                              model="Sarmanov",
#                              measure="OR", method="exact")
# generate a 2X2 panel plot
#par(mfrow=c(2,2))
#plot(single.OR.Jeffreys, type="overlap", xlim=c(0.5, 2),
#    main="Jefferys Prior")
#plot(single.OR.Laplace, type="overlap", xlim=c(0.5, 2),
 #    main="Laplace Prior")
#plot(single.OR.Sar1, type="overlap", xlim=c(0.5, 2),
#     main=expression(paste("Sarmanov Prior ",rho," = 0.5")))
#plot(single.OR.Sar2, type="overlap", xlim=c(0.5, 2),
#     main=expression(paste("Sarmanov Prior ",rho," = -0.5")))

mmeta documentation built on May 29, 2017, 8:28 p.m.